Novak et al. [1]} empirically investigate the relationship between model complexity and generalization of neural networks with piecewise linear activation functions. They propose to use model sensitivity to measure effective complexity. Model sensitivity, also known as robustness, reflects the capacity of a model to distinguish between different inputs at small distances. Novak et al. [1]} introduce two sensitivity metrics, input-output Jacobian norm and trajectory length [3]}.
